DeepAqua: Semantic segmentation of wetland water surfaces with SAR imagery using deep neural networks without manually annotated data

DeepAqua: Semantic segmentation of wetland water surfaces with SAR imagery using deep neural networks without manually annotated data

Deep learning and remote sensing techniques have significantly advanced water surface monitoring; however, the need for annotated data remains a challenge. This is particularly problematic in wetland detection, where water extent varies over time and space, demanding multiple annotations for the sam...

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Bibliographic Details
Main Authors: Francisco J. Peña, Clara Hübinger, Amir H. Payberah, Fernando Jaramillo
Format: Article
Language:English
Published: Elsevier 2024-02-01
Series:International Journal of Applied Earth Observations and Geoinformation
Subjects:
Deep learning
Semantic segmentation
Remote sensing
Wetland mapping
Vegetated water
Automated data labeling
Online Access:http://www.sciencedirect.com/science/article/pii/S156984322300448X

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http://www.sciencedirect.com/science/article/pii/S156984322300448X

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